{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

2_17_09_FeedforwardNetworks_1

2_17_09_FeedforwardNetworks_1 - The simplest neural-network...

Info iconThis preview shows pages 1–12. Sign up to view the full content.

View Full Document Right Arrow Icon
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Background image of page 2
The simplest neural-network model for brain computations is feedforward with one output.
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
The simplest model for the postsynaptic current in a linear feedforward network is dI s dt = I s τ s + w b u b t ( ) τ s b = 1 N τ s dI s dt = I s + w u
Background image of page 4
We assume a steady-state current-to- action-potential-frequency function (the activation function), F(I s ). An extreme model uses very fast firing: For very slow firing: τ s dI s dt = I s + w u with v = F I s ( ) τ r dv dt = v + F I s t ( ) ( ) τ r dv dt = v + F w u ( )
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Neurons can display both slow- and fast-firing properties as the mean input current varies.
Background image of page 6
The simplest neural-network model for brain computations is feedforward with one output.
Background image of page 7

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
A full feedforward network has vector inputs and outputs connected by a weight matrix.
Background image of page 8
For a feedforward network: τ r dv a dt = v a + F W ab b = 1 N a u b τ r d v dt = v + F W u ( )
Background image of page 9

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Reaching with hands is independent of gaze; how does the brain transform coordinates?
Background image of page 10
Premotor cortex neurons encode the site of objects in body-based, not retinal, coordinates.
Background image of page 11

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 12
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}